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Li Q, Lai X, Li T, Madsen KH, Xiao J, Hu K, Feng C, Fu D, Liu X. Brain responses to self- and other- unfairness under resource distribution context: Meta-analysis of fMRI studies. Neuroimage 2024; 297:120707. [PMID: 38942102 DOI: 10.1016/j.neuroimage.2024.120707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 06/06/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024] Open
Abstract
Under resource distribution context, individuals have a strong aversion to unfair treatment not only toward themselves but also toward others. However, there is no clear consensus regarding the commonality and distinction between these two types of unfairness. Moreover, many neuroimaging studies have investigated how people evaluate and respond to unfairness in the abovementioned two contexts, but the consistency of the results remains to be investigated. To resolve these two issues, we sought to summarize existing findings regarding unfairness to self and others and to further elucidate the neural underpinnings related to distinguishing evaluation and response processes through meta-analyses of previous neuroimaging studies. Our results indicated that both types of unfairness consistently activate the affective and conflict-related anterior insula (AI) and dorsal anterior cingulate cortex/supplementary motor area (dACC/SMA), but the activations related to unfairness to self appeared stronger than those related to others, suggesting that individuals had negative reactions to both unfairness and a greater aversive response toward unfairness to self. During the evaluation process, unfairness to self activated the bilateral AI, dACC, and right dorsolateral prefrontal cortex (DLPFC), regions associated with unfairness aversion, conflict, and cognitive control, indicating reactive, emotional and automatic responses. In contrast, unfairness to others activated areas associated with theory of mind, the inferior parietal lobule and temporoparietal junction (IPL-TPJ), suggesting that making rational judgments from the perspective of others was needed. During the response, unfairness to self activated the affective-related left AI and striatum, whereas unfairness to others activated cognitive control areas, the left DLPFC and the thalamus. This indicated that the former maintained the traits of automaticity and emotionality, whereas the latter necessitated cognitive control. These findings provide a fine-grained description of the common and distinct neurocognitive mechanisms underlying unfairness to self and unfairness to others. Overall, this study not only validates the inequity aversion model but also provides direct evidence of neural mechanisms for neurobiological models of fairness.
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Affiliation(s)
- Qi Li
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, PR China
| | - Xinyu Lai
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, PR China; Sino-Danish Center for Education and Research, Beijing, PR China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, PR China; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, PR China
| | - Kristoffer Hougaard Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Jing Xiao
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, PR China
| | - Kesong Hu
- Department of Psychology, University of Arkansas, Little Rock, AR, USA
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
| | - Di Fu
- School of Psychology, University of Surrey, Surrey, England.
| | - Xun Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Marques dos Santos JP, Marques dos Santos JD. Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception. Front Hum Neurosci 2024; 18:1305164. [PMID: 38584851 PMCID: PMC10995351 DOI: 10.3389/fnhum.2024.1305164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/04/2024] [Indexed: 04/09/2024] Open
Abstract
Introduction The research in consumer neuroscience has identified computational methods, particularly artificial intelligence (AI) and machine learning, as a significant frontier for advancement. Previously, we utilized functional magnetic resonance imaging (fMRI) and artificial neural networks (ANNs) to model brain processes related to brand preferences in a paradigm exempted from motor actions. In the current study, we revisit this data, introducing recent advancements in explainable artificial intelligence (xAI) to gain insights into this domain. By integrating fMRI data analysis, machine learning, and xAI, our study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages. Methods We applied independent component analysis (ICA) to overcome the expected fMRI data's high dimensionality, which raises hurdles in AI applications. We extracted pertinent features from the returned ICs. An ANN is then trained on this data, followed by pruning and retraining processes. We then apply explanation techniques, based on path-weights and Shapley values, to make the network more transparent, explainable, and interpretable, and to obtain insights into the underlying brain processes. Results The fully connected ANN model obtained an accuracy of 54.6%, which dropped to 50.4% after pruning. However, the retraining process allowed it to surpass the fully connected network, achieving an accuracy of 55.9%. The path-weights and Shapley-based analysis concludes that, regarding brand perception, the expected initial participation of the primary visual system is followed. Other brain areas participate in early processing and discriminate between preferred and indifferent brands, such as the cuneal and the lateral occipital cortices. Discussion The most important finding is that a split between processing brands|preferred from brands|indifferent may occur during early processing stages, still in the visual system. However, we found no evidence of a "decision pipeline" that would yield if a brand is preferred or indifferent. The results suggest the existence of a "tagging"-like process in parallel flows in the extrastriate. Network training dynamics aggregate specific processes within the hidden nodes by analyzing the model's hidden layer. This yielded that some nodes contribute to both global brand appraisal and specific brand category classification, shedding light on the neural substrates of decision-making in response to brand stimuli.
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Affiliation(s)
- José Paulo Marques dos Santos
- Department of Business Administration, University of Maia, Maia, Portugal
- Unit of Experimental Biology, Faculty of Medicine, University of Porto, Porto, Portugal
- LIACC – Artificial Intelligence and Computer Science Laboratory, University of Porto, Porto, Portugal
- NECE-UBI, Research Centre for Business Sciences, University of Beira Interior, Covilhã, Portugal
| | - José Diogo Marques dos Santos
- Faculty of Engineering, University of Porto, Porto, Portugal
- Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal
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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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Alahmadi AA, Alotaibi NO, Hakami NY, Almutairi RS, Darwesh AM, Abdeen R, Alghamdi J, Abdulaal OM, Alsharif W, Sultan SR, Kanbayti IH. Gender and cytoarchitecture differences: Functional connectivity of the hippocampal sub-regions. Heliyon 2023; 9:e20389. [PMID: 37780771 PMCID: PMC10539667 DOI: 10.1016/j.heliyon.2023.e20389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 07/27/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction The hippocampus plays a significant role in learning, memory encoding, and spatial navigation. Typically, the hippocampus is investigated as a whole region of interest. However, recent work has developed fully detailed atlases based on cytoarchitecture properties of brain regions, and the hippocampus has been sub-divided into seven sub-areas that have structural differences in terms of distinct numbers of cells, neurons, and other structural and chemical properties. Moreover, gender differences are of increasing concern in neuroscience research. Several neuroscience studies have found structural and functional variations between the brain regions of females and males, and the hippocampus is one of these regions. Aim The aim of this study to explore whether the cytoarchitecturally distinct sub-regions of the hippocampus have varying patterns of functional connectivity with different networks of the brain and how these functional connections differ in terms of gender differences. Method This study investigated 200 healthy participants using seed-based resting-state functional magnetic resonance imaging (rsfMRI). The primary aim of this study was to explore the resting connectivity and gender distinctions associated with specific sub-regions of the hippocampus and their relationship with major functional brain networks. Results The findings revealed that the majority of the seven hippocampal sub-regions displayed functional connections with key brain networks, and distinct patterns of functional connectivity were observed between the hippocampal sub-regions and various functional networks within the brain. Notably, the default and visual networks exhibited the most consistent functional connections. Additionally, gender-based analysis highlighted evident functional resemblances and disparities, particularly concerning the anterior section of the hippocampus. Conclusion This study highlighted the functional connectivity patterns and involvement of the hippocampal sub-regions in major brain functional networks, indicating that the hippocampus should be investigated as a region of multiple distinct functions and should always be examined as sub-regions of interest. The results also revealed clear gender differences in functional connectivity.
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Affiliation(s)
- Adnan A.S. Alahmadi
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Nada O. Alotaibi
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Norah Y. Hakami
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Raghad S. Almutairi
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Afnan M.F. Darwesh
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rawan Abdeen
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jamaan Alghamdi
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Osamah M. Abdulaal
- Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madina, Saudi Arabia
| | - Walaa Alsharif
- Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madina, Saudi Arabia
| | - Salahaden R. Sultan
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ibrahem H. Kanbayti
- Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Nong H, Pang X, Jing J, Cen Y, Qin S, Jiang H. Alterations in intra- and inter-network connectivity associated with cognition impairment in insulinoma patients. Front Endocrinol (Lausanne) 2023; 14:1234921. [PMID: 37818091 PMCID: PMC10561291 DOI: 10.3389/fendo.2023.1234921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/06/2023] [Indexed: 10/12/2023] Open
Abstract
Objective Cognitive dysfunction is common in insulinoma patients, but the underlying neural mechanisms are less well understood. This study aimed to explore the alterations of intra- and inter-network connectivity patterns associated with patients with insulinoma. Methods Resting-state fMRI were acquired from 13 insulinoma patients and 13 matched healthy controls (HCs). Group Independent component analysis (ICA) was employed to capture the resting-state networks (RSNs), then the intra- and inter-network connectivity patterns, were calculated and compared. Montreal Cognitive Assessment (MoCA) was used to assess the cognitive function. The relationship between connectivity patterns and MoCA scores was also examined. Results Insulinoma patients performed significantly worse on MoCA compared to HCs. The intra-network connectivity analysis revealed that patients with insulinoma showed decreased connectivity in the left medial superior frontal gyrus within anterior default mode network (aDMN), and decreased connectivity in right lingual gyrus within the visual network (VN). The intra-network connectivity analysis showed that patients with insulinoma had an increased connectivity between the inferior-posterior default mode network (ipDMN) and right frontoparietal network (rFPN) and decreased connectivity between the ipDMN and auditory network (AUN). There was a significant negative correlation between the ipDMN-rFPN connectivity and MoCA score. Conclusion This study demonstrated significant abnormalities in the intra- and inter-network connectivity in patients with insulinoma, which may represent the neural mechanisms underlying the cognitive impairment in insulinoma patients.
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Affiliation(s)
- Hui Nong
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Xiaomin Pang
- Department of Neurology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Jie Jing
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Yu Cen
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Shanyu Qin
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
| | - Haixing Jiang
- Department of Gastroenterology, Guangxi Medical University First Affiliated Hospital, Nanning, China
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Hilger K, Euler MJ. Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability? J Cogn Neurosci 2022; 35:1-17. [PMID: 36473095 DOI: 10.1162/jocn_a_01946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
EEG has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The MMN is an ERP that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans' dominant sensory modality-vision. EEG was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations and deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual MMN were calculated with and without Laplacian transformation. Correlations between visual MMN and intelligence (Raven's Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field.
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Affiliation(s)
- Kirsten Hilger
- Julius-Maximilians University of Würzburg, Germany
- Goethe University, Frankfurt Germany
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Sendi MSE, Salat DH, Miller RL, Calhoun VD. Two-step clustering-based pipeline for big dynamic functional network connectivity data. Front Neurosci 2022; 16:895637. [PMID: 35958983 PMCID: PMC9358255 DOI: 10.3389/fnins.2022.895637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power. Methods In the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP). Results We found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP. Conclusion We developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- *Correspondence: Mohammad S. E. Sendi,
| | - David H. Salat
- Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Vince D. Calhoun,
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Frischkorn GT, Hilger K, Kretzschmar A, Schubert AL. Intelligenzdiagnostik der Zukunft. PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.
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Affiliation(s)
| | - Kirsten Hilger
- Institut für Psychologie, Universität Würzburg, Deutschland
| | | | - Anna-Lena Schubert
- Psychologisches Institut, Universität Heidelberg, Deutschland
- Psychologisches Institut, Universität Mainz, Deutschland
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State-dependent effects of neural stimulation on brain function and cognition. Nat Rev Neurosci 2022; 23:459-475. [PMID: 35577959 DOI: 10.1038/s41583-022-00598-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 01/02/2023]
Abstract
Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such 'state-dependent' changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.
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Oerlemans AJM, Barendregt DMH, Kooijman SC, Bunnik EM. Impact of incidental findings on young adult participants in brain imaging research: an interview study. Eur Radiol 2022; 32:3839-3845. [DOI: 10.1007/s00330-021-08474-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/25/2021] [Accepted: 11/19/2021] [Indexed: 01/02/2023]
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Han H. Cerebellum and Emotion in Morality. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1378:179-194. [DOI: 10.1007/978-3-030-99550-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Tahmasian M, Aleman A, Andreassen OA, Arab Z, Baillet M, Benedetti F, Bresser T, Bright J, Chee MW, Chylinski D, Cheng W, Deantoni M, Dresler M, Eickhoff SB, Eickhoff CR, Elvsåshagen T, Feng J, Foster-Dingley JC, Ganjgahi H, Grabe HJ, Groenewold NA, Ho TC, Hong SB, Houenou J, Irungu B, Jahanshad N, Khazaie H, Kim H, Koshmanova E, Kocevska D, Kochunov P, Lakbila-Kamal O, Leerssen J, Li M, Luik AI, Muto V, Narbutas J, Nilsonne G, O’Callaghan VS, Olsen A, Osorio RS, Poletti S, Poudel G, Reesen JE, Reneman L, Reyt M, Riemann D, Rosenzweig I, Rostampour M, Saberi A, Schiel J, Schmidt C, Schrantee A, Sciberras E, Silk TJ, Sim K, Smevik H, Soares JC, Spiegelhalder K, Stein DJ, Talwar P, Tamm S, Teresi GI, Valk SL, Van Someren E, Vandewalle G, Van Egroo M, Völzke H, Walter M, Wassing R, Weber FD, Weihs A, Westlye LT, Wright MJ, Wu MJ, Zak N, Zarei M. ENIGMA-Sleep: Challenges, opportunities, and the road map. J Sleep Res 2021; 30:e13347. [PMID: 33913199 PMCID: PMC8803276 DOI: 10.1111/jsr.13347] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/14/2021] [Accepted: 03/16/2021] [Indexed: 12/26/2022]
Abstract
Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.
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Affiliation(s)
- Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - André Aleman
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Inst of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zahra Arab
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Marion Baillet
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Francesco Benedetti
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Tom Bresser
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Joanna Bright
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Michael W.L. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Daphne Chylinski
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
| | - Michele Deantoni
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty,, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Claudia R. Eickhoff
- Institute of Neuroscience and Medicine, Structural and functional organisation of the brain (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Torbjørn Elvsåshagen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jessica C. Foster-Dingley
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, UK
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Nynke A. Groenewold
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Tiffany C. Ho
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Seung Bong Hong
- Department of Neurology, Samsung Medical Center, SBRI (Samsung Biomedical Research Institute), Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Josselin Houenou
- Univ Paris Saclay, NeuroSpin neuroimaging platform, Psychiatry Team, UNIACT Lab, CEA Saclay, Gif-Sur-Yvette Cedex, France
- DMU IMPACT de Psychiatrie et d'Addictologie, APHP, Hôpitaux Universitaires Mondor, Créteil, France
- Univ Paris Est Créteil, INSERM U 955, IMRB Team 15 « Translational Neuropsychiatry », Foundation FondaMental, Créteil, France
| | - Benson Irungu
- Department of Psychiatry & Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Hosung Kim
- Laboratory of Neuro Imaging at USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ekaterina Koshmanova
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Desi Kocevska
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Oti Lakbila-Kamal
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Jeanne Leerssen
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Meng Li
- Clinical Affective Neuroimaging Laboratory, Otto von Guericke University, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annemarie I. Luik
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Vincenzo Muto
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Justinas Narbutas
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, Stress Research Institute, Stockholm University, Stockholm, Sweden
| | | | - Alexander Olsen
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ricardo S. Osorio
- Healthy Brain Aging and Sleep Center, Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Sara Poletti
- Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Govinda Poudel
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Vic., Australia
| | - Joyce E. Reesen
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
| | - Mathilde Reyt
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liège, Liège, Belgium
| | - Dieter Riemann
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Masoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Amin Saberi
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Julian Schiel
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Christina Schmidt
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
- Psychology and Neuroscience of Cognition Research Unit, Faculty of Psychology and Educational Sciences, University of Liège, Liège, Belgium
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
| | - Emma Sciberras
- Department of Paediatrics, University of Melbourne, Parkville, Vic., Australia
- Murdoch Children's Research Institute, Parkville, Vic., Australia
- School of Psychology, Deakin University, Geelong, Vic., Australia
| | - Tim J. Silk
- Department of Paediatrics, University of Melbourne, Parkville, Vic., Australia
- Murdoch Children's Research Institute, Parkville, Vic., Australia
- School of Psychology, Deakin University, Geelong, Vic., Australia
| | - Kang Sim
- Institute of Mental Health, Buangkok, Singapore
| | - Hanne Smevik
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jair C. Soares
- Department of Psychiatry & Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kai Spiegelhalder
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Dan J. Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Puneet Talwar
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Sandra Tamm
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, Stress Research Institute, Stockholm University, Stockholm, Sweden
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Giana I. Teresi
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Sofie L. Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty,, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Eus Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
- Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
| | - Gilles Vandewalle
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Maxime Van Egroo
- GIGA-Institute, Cyclotron Research Center/In Vivo Imaging, Sleep and Chronobiology Lab, University of Liège, Liège, Belgium
| | - Henry Völzke
- Institute for Community Medicine, Department SHIP/Clinical Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Greifswald, Germany
| | - Martin Walter
- Clinical Affective Neuroimaging Laboratory, Otto von Guericke University, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Rick Wassing
- Department of Sleep and Circadian Research, Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia
| | - Frederik D. Weber
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Antoine Weihs
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Lars Tjelta Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Inst of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Margaret J. Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Qld, Australia
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Qld, Australia
| | - Mon-Ju Wu
- Department of Psychology and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Nathalia Zak
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Inst of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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15
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Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med 2021; 135:104660. [PMID: 34346319 DOI: 10.1016/j.compbiomed.2021.104660] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 02/07/2023]
Abstract
The growth of artificial intelligence in promoting healthcare is rapidly progressing. Notwithstanding its promising nature, however, AI in healthcare embodies certain ethical challenges as well. This research aims to delineate the most influential elements of scientific research on AI ethics in healthcare by conducting bibliometric, social network analysis, and cluster-based content analysis of scientific articles. Not only did the bibliometric analysis identify the most influential authors, countries, institutions, sources, and documents, but it also recognized four ethical concerns associated with 12 medical issues. These ethical categories are composed of normative, meta-ethics, epistemological and medical practice. The content analysis complemented this list of ethical categories and distinguished seven more ethical categories: ethics of relationships, medico-legal concerns, ethics of robots, ethics of ambient intelligence, patients' rights, physicians' rights, and ethics of predictive analytics. This analysis likewise identified 40 general research gaps in the literature and plausible future research strands. This analysis furthers conversations on the ethics of AI and associated emerging technologies such as nanotech and biotech in healthcare, hence, advances convergence research on the ethics of AI in healthcare. Practically, this research will provide a map for policymakers and AI engineers and scientists on what dimensions of AI-based medical interventions require stricter policies and guidelines and robust ethical design and development.
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Affiliation(s)
- Tahereh Saheb
- Management Studies Center, Tarbiat Modares University, Tehran, Iran.
| | - Tayebeh Saheb
- Assistant professor, Faculty of Law, Tarbiat Modares University, Tehran, Iran.
| | - David O Carpenter
- Director for the Institute for Health and the Environment, School of Public Health, State University of New York, University at Albany, USA.
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16
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Smallwood J, Turnbull A, Wang HT, Ho NS, Poerio GL, Karapanagiotidis T, Konu D, Mckeown B, Zhang M, Murphy C, Vatansever D, Bzdok D, Konishi M, Leech R, Seli P, Schooler JW, Bernhardt B, Margulies DS, Jefferies E. The neural correlates of ongoing conscious thought. iScience 2021; 24:102132. [PMID: 33665553 PMCID: PMC7907463 DOI: 10.1016/j.isci.2021.102132] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
A core goal in cognitive neuroscience is identifying the physical substrates of the patterns of thought that occupy our daily lives. Contemporary views suggest that the landscape of ongoing experience is heterogeneous and can be influenced by features of both the person and the context. This perspective piece considers recent work that explicitly accounts for both the heterogeneity of the experience and context dependence of patterns of ongoing thought. These studies reveal that systems linked to attention and control are important for organizing experience in response to changing environmental demands. These studies also establish a role of the default mode network beyond task-negative or purely episodic content, for example, implicating it in the level of vivid detail in experience in both task contexts and in spontaneous self-generated experiential states. Together, this work demonstrates that the landscape of ongoing thought is reflected in the activity of multiple neural systems, and it is important to distinguish between processes contributing to how the experience unfolds from those linked to how these experiences are regulated.
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Affiliation(s)
- Jonathan Smallwood
- Department of Psychology / York Imaging Centre, University of York, York, England
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Adam Turnbull
- Department of Psychology / York Imaging Centre, University of York, York, England
- University of Rochester School of Nursing, Rochester, NY, USA
| | | | - Nerissa S.P. Ho
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Giulia L. Poerio
- Department of Psychology, University of Essex, Colchester, England
| | | | - Delali Konu
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Brontë Mckeown
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Meichao Zhang
- Department of Psychology / York Imaging Centre, University of York, York, England
| | | | | | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Mahiko Konishi
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Department d'Etudes Cognitives, ENS, PSL University, EHESS, CNRS, Paris, France
| | | | | | - Jonathan W. Schooler
- Department of Psychology, duke University, Durham, NC, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Boris Bernhardt
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel S. Margulies
- Centre Nationale de la Researche Scientifique, Institute du Cerveau et de la Moelle epiniere, Paris, France
| | - Elizabeth Jefferies
- Department of Psychology / York Imaging Centre, University of York, York, England
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17
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Weigard A, Wilson SJ, Shapiro Z, Galloway-Long H, Huang-Pollock C. Neural correlates of working memory's suppression of aversive olfactory distraction effects. Brain Imaging Behav 2021; 15:2254-2268. [PMID: 33405095 DOI: 10.1007/s11682-020-00419-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2020] [Indexed: 10/22/2022]
Abstract
Human cognitive performance is often disrupted by distractions related to aversive stimuli and affective states, but, paradoxically, there is also evidence to suggest that high working memory demands reduce the impact of aversive distraction. Previous empirical work suggests this latter effect occurs because working memory demands reduce attention to off-task processes, but the brain regions that mediate this effect remain uncertain. The current study utilizes a novel distraction manipulation involving unpleasant odorants to identify neural structures that buffer performance from aversive distraction under high working memory demands, and to clarify their connectivity in this context. Twenty-one healthy young adults (12 women) completed a verbal n-back task under two levels of load and were concurrently exposed to either room air or aversive odorants. Three brain regions displayed increases in neural responses to olfactory distractors under high load only; the left dorsolateral prefrontal cortex, the left ventrolateral prefrontal cortex and right cerebellar Crus I. Of these regions, only the ventrolateral prefrontal cortex also displayed context-specific connectivity with a region thought to be involved in off-task processes: the dorsomedial prefrontal cortex. Overall, results suggest that, under high working memory demands, areas of the prefrontal cortex and cerebellum shield cognition from aversive distraction, potentially through interactions with brain structures involved in off-task processes.
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Affiliation(s)
- Alexander Weigard
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, Ann Arbor, MI, 48109, USA.
| | - Stephen J Wilson
- Department of Psychology, Pennsylvania State University, Moore Building, State College, PA, USA
| | - Zvi Shapiro
- Department of Psychology, Pennsylvania State University, Moore Building, State College, PA, USA
| | - Hilary Galloway-Long
- Department of Psychology, Pennsylvania State University, Moore Building, State College, PA, USA
| | - Cynthia Huang-Pollock
- Department of Psychology, Pennsylvania State University, Moore Building, State College, PA, USA
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18
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Using mouse cursor tracking to investigate online cognition: Preserving methodological ingenuity while moving toward reproducible science. Psychon Bull Rev 2020; 28:766-787. [PMID: 33319317 PMCID: PMC8219569 DOI: 10.3758/s13423-020-01851-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/13/2020] [Indexed: 01/21/2023]
Abstract
Mouse cursor tracking has become a prominent method for characterizing cognitive processes, used in a wide variety of domains of psychological science. Researchers have demonstrated considerable ingenuity in the application of the approach, but the methodology has not undergone systematic analysis to facilitate the development of best practices. Furthermore, recent research has demonstrated effects of experimental design features on a number of mousetracking outcomes. We conducted a systematic review of the mouse-tracking literature to survey the reporting and spread of mouse variables (Cursor speed, Sampling rate, Training), physical characteristics of the experiments (Stimulus position, Response box position) and response requirements (Start procedure, Response procedure, Response deadline). This survey reveals that there is room for improvement in reporting practices, especially of subtler design features that researchers may have assumed would not impact research results (e.g., Cursor speed). We provide recommendations for future best practices in mouse-tracking studies and consider how best to standardize the mouse-tracking literature without excessively constraining the methodological flexibility that is essential to the field.
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19
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Cao CC, Reimann M. Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data. Front Psychol 2020; 11:550204. [PMID: 33224048 PMCID: PMC7674591 DOI: 10.3389/fpsyg.2020.550204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 10/08/2020] [Indexed: 11/13/2022] Open
Abstract
This article reviews a wide range of functional magnetic resonance imaging (fMRI) studies conducted in the field of consumer neuroscience to (1) highlight common interpretative approaches of neuroimaging data (i.e., forward inference and reverse inference), (2) discuss potential interpretative issues associated with these approaches, and (3) provide a framework that employs a multi-method approach aimed to possibly raise the explanatory power and, thus, the validity of functional neuroimaging research in consumer neuroscience. Based on this framework, we argue that the validity of fMRI studies can be improved by the triangulation of (1) careful design of neuroimaging studies and analyses of data, (2) meta-analyses, and (3) the integration of psychometric and behavioral data with neuroimaging data. Guidelines on when and how to employ triangulation methods on neuroimaging data are included. Moreover, we also included discussions on practices and research directions that validate fMRI studies in consumer neuroscience beyond data triangulation.
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Affiliation(s)
- C. Clark Cao
- Department of Marketing and International Business, Lingnan University, Tuen Mun, Hong Kong
| | - Martin Reimann
- Department of Marketing, University of Arizona, Tucson, AZ, United States
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20
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Clark EA, Kessinger J, Duncan SE, Bell MA, Lahne J, Gallagher DL, O'Keefe SF. The Facial Action Coding System for Characterization of Human Affective Response to Consumer Product-Based Stimuli: A Systematic Review. Front Psychol 2020; 11:920. [PMID: 32528361 PMCID: PMC7264164 DOI: 10.3389/fpsyg.2020.00920] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/14/2020] [Indexed: 12/13/2022] Open
Abstract
To characterize human emotions, researchers have increasingly utilized Automatic Facial Expression Analysis (AFEA), which automates the Facial Action Coding System (FACS) and translates the facial muscular positioning into the basic universal emotions. There is broad interest in the application of FACS for assessing consumer expressions as an indication of emotions to consumer product-stimuli. However, the translation of FACS to characterization of emotions is elusive in the literature. The aim of this systematic review is to give an overview of how FACS has been used to investigate human emotional behavior to consumer product-based stimuli. The search was limited to studies published in English after 1978, conducted on humans, using FACS or its action units to investigate affect, where emotional response is elicited by consumer product-based stimuli evoking at least one of the five senses. The search resulted in an initial total of 1,935 records, of which 55 studies were extracted and categorized based on the outcomes of interest including (i) method of FACS implementation; (ii) purpose of study; (iii) consumer product-based stimuli used; and (iv) measures of affect validation. Most studies implemented FACS manually (73%) to develop products and/or software (20%) and used consumer product-based stimuli that had known and/or defined capacity to evoke a particular affective response, such as films and/or movie clips (20%); minimal attention was paid to consumer products with low levels of emotional competence or with unknown affective impact. The vast majority of studies (53%) did not validate FACS-determined affect and, of the validation measures that were used, most tended to be discontinuous in nature and only captured affect as it holistically related to an experience. This review illuminated some inconsistencies in how FACS is carried out as well as how emotional response is inferred from facial muscle activation. This may prompt researchers to consider measuring the total consumer experience by employing a variety of methodologies in addition to FACS and its emotion-based interpretation guide. Such strategies may better conceptualize consumers' experience with products of low, unknown, and/or undefined capacity to evoke an affective response such as product prototypes, line extensions, etc.
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Affiliation(s)
- Elizabeth A. Clark
- Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
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21
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Ghosts in machine learning for cognitive neuroscience: Moving from data to theory. Neuroimage 2018; 180:88-100. [DOI: 10.1016/j.neuroimage.2017.08.019] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 07/17/2017] [Accepted: 08/04/2017] [Indexed: 12/17/2022] Open
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22
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Kragel PA, Koban L, Barrett LF, Wager TD. Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging. Neuron 2018; 99:257-273. [PMID: 30048614 PMCID: PMC6296466 DOI: 10.1016/j.neuron.2018.06.009] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 06/01/2018] [Accepted: 06/05/2018] [Indexed: 01/22/2023]
Abstract
Human neuroimaging research has transitioned from mapping local effects to developing predictive models of mental events that integrate information distributed across multiple brain systems. Here we review work demonstrating how multivariate predictive models have been utilized to provide quantitative, falsifiable predictions; establish mappings between brain and mind with larger effects than traditional approaches; and help explain how the brain represents mental constructs and processes. Although there is increasing progress toward the first two of these goals, models are only beginning to address the latter objective. By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.
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Affiliation(s)
- Philip A Kragel
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA; Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Leonie Koban
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Tor D Wager
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA.
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Hsu MYT. RETRACTED: Cognitive systems research for neuromarketing assessment on evaluating consumer learning theory with fMRI: Comparing how two Word-Of-Mouth strategies affect the human brain differently after a product harm crisis. COGN SYST RES 2018. [DOI: 10.1016/j.cogsys.2017.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Bednarz HM, Kana RK. Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders. Neurosci Biobehav Rev 2018; 90:50-69. [PMID: 29608989 DOI: 10.1016/j.neubiorev.2018.03.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/26/2018] [Accepted: 03/22/2018] [Indexed: 10/17/2022]
Abstract
Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.
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Affiliation(s)
- Haley M Bednarz
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rajesh K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
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25
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Guyon H, Kop JL, Juhel J, Falissard B. Measurement, ontology, and epistemology: Psychology needs pragmatism-realism. THEORY & PSYCHOLOGY 2018. [DOI: 10.1177/0959354318761606] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Measurement in psychology is at the heart of a major debate in the academic literature. We aim to contribute to a critical discussion of this issue. We propose to reposition the object of this type of measure, namely a mental attribute as measured by mental tests. Mental attributes should be considered not as a true object independent of the knower, but as an emergent property of a person dependent on the social context. On the basis of this clarified ontology, we consider that an empirical approach to measuring a mental attribute is possible. This approach must be resolutely pragmatist and realist. In practical terms, this means that a test needs to be renegotiated relative to the context. The validation of quantitative measures requires verification of a certain number of criteria. Consequently, our work critically explores measures as they are usually implemented in the area of psychometrics.
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26
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Non-invasive imaging modalities to study neurodegenerative diseases of aging brain. J Chem Neuroanat 2018; 95:54-69. [PMID: 29474853 DOI: 10.1016/j.jchemneu.2018.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Revised: 02/16/2018] [Accepted: 02/16/2018] [Indexed: 12/13/2022]
Abstract
The aim of this article is to highlight current approaches for imaging elderly brain, indispensable for cognitive neuroscience research with emphasis on the basic physical principles of various non-invasive neuroimaging techniques. The first part of this article presents a quick overview of the primary non-invasive neuroimaging modalities used by cognitive neuroscientists such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), electroencephalography (EEG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance spectroscopic imaging (MRSI), Profusion imaging, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS) and diffusion tensor imaging (DTI) along with tractography and connectomics. The second part provides a comprehensive overview of different multimodality imaging techniques for various cognitive neuroscience studies of aging brain.
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27
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Spence MJ, Granier-Deferre C, Schaal B. L’étude du comportement est unique pour comprendre la cognition fœtale et néonatale – L’imagerie cérébrale la complète lorsqu’elle s’inspire de validité écologique. ENFANCE 2017. [DOI: 10.3917/enf1.173.0307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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28
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van Ginneken WF, Poolton JM, Capio CM, van der Kamp J, Choi CSY, Masters RSW. Conscious Control Is Associated With Freezing of Mechanical Degrees of Freedom During Motor Learning. J Mot Behav 2017; 50:436-456. [PMID: 28925825 DOI: 10.1080/00222895.2017.1365045] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This study investigated whether conscious control is associated with freezing of mechanical degrees of freedom during motor learning. Participants practiced a throwing task using either error-strewn or error-reduced practice protocols, which encourage high or low levels of conscious control, respectively. After 24 hr, participants engaged in a series of delayed retention and transfer tests. Furthermore, propensity for conscious control was assessed using participants' ratings and freezing was gauged through movement variability of the throwing arm. Performance was defined by mean radial error. In the error-strewn group, propensity for conscious control was positively associated with both freezing and performance. In the error-reduced group, propensity for conscious control was negatively associated with performance, but not with freezing. These results suggest that conscious control is associated with freezing of mechanical degrees of freedom during motor learning.
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Affiliation(s)
| | - Jamie M Poolton
- a The University of Hong Kong , School of Public Health.,b Leeds Beckett University , Cargegie School of Sport , United Kingdom
| | - Catherine M Capio
- a The University of Hong Kong , School of Public Health.,c The University of Waikato , Faculty of Health, Sport and Human Performance , New Zealand
| | - John van der Kamp
- a The University of Hong Kong , School of Public Health.,d VU University Amsterdam , Faculty of Behavioural and Movement Sciences , The Netherlands
| | | | - Richard S W Masters
- a The University of Hong Kong , School of Public Health.,c The University of Waikato , Faculty of Health, Sport and Human Performance , New Zealand
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29
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Evolutionary perspective for optimal selection of EEG electrodes and features. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.03.022] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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30
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Safron A, Sylva D, Klimaj V, Rosenthal AM, Li M, Walter M, Bailey JM. Neural Correlates of Sexual Orientation in Heterosexual, Bisexual, and Homosexual Men. Sci Rep 2017; 7:41314. [PMID: 28145518 PMCID: PMC5286516 DOI: 10.1038/srep41314] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 12/14/2016] [Indexed: 12/30/2022] Open
Abstract
Studies of subjective and genital sexual arousal in monosexual (i.e. heterosexual and homosexual) men have repeatedly found that erotic stimuli depicting men’s preferred sex produce strong responses, whereas erotic stimuli depicting the other sex produce much weaker responses. Inconsistent results have previously been obtained in bisexual men, who have sometimes demonstrated distinctly bisexual responses, but other times demonstrated patterns more similar to those observed in monosexual men. We used fMRI to investigate neural correlates of responses to erotic pictures and videos in heterosexual, bisexual, and homosexual men, ages 25–50. Sixty participants were included in video analyses, and 62 were included in picture analyses. We focused on the ventral striatum (VS), due to its association with incentive motivation. Patterns were consistent with sexual orientation, with heterosexual and homosexual men showing female-favoring and male-favoring responses, respectively. Bisexual men tended to show less differentiation between male and female stimuli. Consistent patterns were observed in the whole brain, including the VS, and also in additional regions such as occipitotemporal, anterior cingulate, and orbitofrontal cortices. This study extends previous findings of gender-specific neural responses in monosexual men, and provides initial evidence for distinct brain activity patterns in bisexual men.
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Affiliation(s)
- Adam Safron
- Department of Psychology, Northwestern University, USA
| | - David Sylva
- Department of Psychology, Northwestern University, USA.,Department of Psychiatry, Kaiser Permanente, USA
| | | | - A M Rosenthal
- Department of Psychology, Northwestern University, USA.,Department of Psychiatry, Kaiser Permanente, USA
| | - Meng Li
- Department of Neurology, Otto von Guericke University Magdeburg, Germany
| | - Martin Walter
- Department of Neurology, Otto von Guericke University Magdeburg, Germany
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31
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Schoentgen B, Lancelot C, Le Gall D. [Eating behavior in pediatric obesity: Of the advantages of combining the neurobiological and neuropsychological approaches]. Arch Pediatr 2017; 24:273-279. [PMID: 28131560 DOI: 10.1016/j.arcped.2016.12.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 07/15/2016] [Accepted: 12/06/2016] [Indexed: 11/27/2022]
Abstract
Obesity is currently considered a major public health concern, as shown by the growing number of people with excess weight, alarmingly in infants, and despite medical care. Many studies have underlined the reasons for medical care failure caused by an inability to sustain a resistant behavior towards palatable food (high sugar and fat content). Hence, previous research has highlighted connections between excessive eating behavior and the activity of neurotransmitters in brain areas involved in affective behavior and the reward circuit. Reduction of the dopaminergic activity in the prefrontal orbital and limbic cortex may raise the question of executive skills, which play a major role in social adaptation and behavior control. These findings remind us of the need to grasp environmental and behavioral factors to better identify cognitive and affective profiles and improve childhood obesity treatment.
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Affiliation(s)
- B Schoentgen
- Laboratoire de psychologie des Pays-de-la-Loire, EA4638, université d'Angers, 11, boulevard Lavoisier, 49045 Angers cedex 01, France.
| | - C Lancelot
- Laboratoire de psychologie des Pays-de-la-Loire, EA4638, université d'Angers, 11, boulevard Lavoisier, 49045 Angers cedex 01, France
| | - D Le Gall
- Laboratoire de psychologie des Pays-de-la-Loire, EA4638, université d'Angers, 11, boulevard Lavoisier, 49045 Angers cedex 01, France
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32
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Pirimoglu B, Sade R, Ogul H, Kantarci M, Eren S, Levent A. How Can New Imaging Modalities Help in the Practice of Radiology? Eurasian J Med 2017; 48:213-221. [PMID: 28149149 DOI: 10.5152/eajm.2016.0260] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The purpose of this article was to provide an up-to-date review on the spectrum of new imaging applications in the practice of radiology. New imaging techniques have been developed with the objective of obtaining structural and functional analyses of different body systems. Recently, new imaging modalities have aroused the interest of many researchers who are studying the applicability of these modalities in the evaluation of different organs and diseases. In this review article, we present the efficiency and utilization of current imaging modalities in daily radiological practice.
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Affiliation(s)
- Berhan Pirimoglu
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
| | - Recep Sade
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
| | - Hayri Ogul
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
| | - Mecit Kantarci
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
| | - Suat Eren
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
| | - Akın Levent
- Department of Radiology, Ataturk University School of Medicine, Erzurum, Turkey
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Cacha LA, Parida S, Dehuri S, Cho SB, Poznanski RR. A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects. J Integr Neurosci 2016; 15:593-606. [DOI: 10.1142/s0219635216500345] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- L. A. Cacha
- Laser Center, Faculty of Science, Ibnu Sina Institute for Scientific Industrial Research (ISI-SIR), Universiti Teknologi, Malaysia
| | - S. Parida
- Carrier Software and Core Network Department, Huawei Technologies India Pvt Ltd Near EPIP Industrial Area, Whitefield Bangalore – 560 066, Karnataka, India
| | - S. Dehuri
- Department of Systems Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, South Korea
| | - S.-B. Cho
- Department of Computer Science, Yonsei University Seoul, South Korea
| | - R. R. Poznanski
- Department of Clinical Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi, Malaysia 81310 Johor Bahru, Johor, Malaysia
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Soares JM, Magalhães R, Moreira PS, Sousa A, Ganz E, Sampaio A, Alves V, Marques P, Sousa N. A Hitchhiker's Guide to Functional Magnetic Resonance Imaging. Front Neurosci 2016; 10:515. [PMID: 27891073 PMCID: PMC5102908 DOI: 10.3389/fnins.2016.00515] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 10/25/2016] [Indexed: 12/12/2022] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.
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Affiliation(s)
- José M. Soares
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Pedro S. Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Alexandre Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
- Department of Informatics, University of MinhoBraga, Portugal
| | - Edward Ganz
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Adriana Sampaio
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of MinhoBraga, Portugal
| | - Victor Alves
- Department of Informatics, University of MinhoBraga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBraga, Portugal
- ICVS/3B's - PT Government Associate LaboratoryBraga, Portugal
- Clinical Academic Center – BragaBraga, Portugal
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Papoti D, Yen CCC, Hung CC, Ciuchta J, Leopold DA, Silva AC. Design and implementation of embedded 8-channel receive-only arrays for whole-brain MRI and fMRI of conscious awake marmosets. Magn Reson Med 2016; 78:387-398. [PMID: 27501382 DOI: 10.1002/mrm.26339] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 05/20/2016] [Accepted: 06/19/2016] [Indexed: 01/03/2023]
Abstract
PURPOSE The common marmoset (Callithrix jacchus) is a New World primate of increasing interest to neuroscience and in translational brain research. The present work describes the design and implementation of individualized 8-channel receive-only radiofrequency (RF) coil arrays that provide whole-brain coverage and allow anatomical and functional MRI experiments in conscious, awake marmosets. METHODS The coil arrays were designed with their elements embedded inside individualized restraint helmets. The size, geometry, and arrangement of the coil elements were optimized to allow whole-brain coverage. Coil-to-coil decoupling was achieved by a combination of geometric decoupling and low input impedance preamplifiers. The performance of the embedded arrays was compared against that of one 8-channel receive-only array built to fit the external surface of the helmets. RESULTS Three individualized helmets with embedded coil arrays were built for three marmosets. Whole-brain coverage was achieved with high sensitivity extending over the entire cortex. Visual stimulation of conscious awake marmosets elicited robust BOLD fMRI responses in both primary and higher order visual areas of the occipitotemporal cortex. CONCLUSION The high sensitivity provided by embedded receive-only coil arrays allows both anatomical and functional MRI data to be obtained with high spatial resolution in conscious, awake marmosets. Magn Reson Med 78:387-398, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Daniel Papoti
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Cecil Chern-Chyi Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Chia-Chun Hung
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.,Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer Ciuchta
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - David A Leopold
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | - Afonso C Silva
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
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36
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Feng S, Holmes P. Will big data yield new mathematics? An evolving synergy with neuroscience. IMA JOURNAL OF APPLIED MATHEMATICS 2016; 81:432-456. [PMID: 27516705 PMCID: PMC4975073 DOI: 10.1093/imamat/hxw026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Indexed: 06/06/2023]
Abstract
New mathematics has often been inspired by new insights into the natural world. Here we describe some ongoing and possible future interactions among the massive data sets being collected in neuroscience, methods for their analysis and mathematical models of the underlying, still largely uncharted neural substrates that generate these data. We start by recalling events that occurred in turbulence modelling when substantial space-time velocity field measurements and numerical simulations allowed a new perspective on the governing equations of fluid mechanics. While no analogous global mathematical model of neural processes exists, we argue that big data may enable validation or at least rejection of models at cellular to brain area scales and may illuminate connections among models. We give examples of such models and survey some relatively new experimental technologies, including optogenetics and functional imaging, that can report neural activity in live animals performing complex tasks. The search for analytical techniques for these data is already yielding new mathematics, and we believe their multi-scale nature may help relate well-established models, such as the Hodgkin-Huxley equations for single neurons, to more abstract models of neural circuits, brain areas and larger networks within the brain. In brief, we envisage a closer liaison, if not a marriage, between neuroscience and mathematics.
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Affiliation(s)
- S Feng
- Department of Applied Mathematics and Sciences, Khalifa University of Science, Technology, and Research, Abu Dhabi, United Arab Emirates
| | - P Holmes
- Program in Applied and Computational Mathematics, Department of Mechanical and Aerospace Engineering and Princeton Neuroscience Institute, Princeton University, NJ 08544
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37
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Smith DV, Gseir M, Speer ME, Delgado MR. Toward a cumulative science of functional integration: A meta-analysis of psychophysiological interactions. Hum Brain Mapp 2016; 37:2904-17. [PMID: 27145472 DOI: 10.1002/hbm.23216] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 04/02/2016] [Accepted: 04/04/2016] [Indexed: 01/31/2023] Open
Abstract
Much of the work in cognitive neuroscience is shifting from a focus on single brain regions to a focus on the connectivity between multiple brain regions. These inter-regional connectivity patterns contribute to a wide range of behaviors and are studied with models of functional integration. The rapid expansion of the literature on functional integration offers an opportunity to scrutinize the consistency and specificity of one of the most popular approaches for quantifying connectivity: psychophysiological interaction (PPI) analysis. We performed coordinate-based meta-analyses on 284 PPI studies, which allowed us to test (a) whether those studies consistently converge on similar target regions and (b) whether the identified target regions are specific to the chosen seed region and psychological context. Our analyses revealed two key results. First, we found that different types of PPI studies-e.g., those using seeds such as amygdala and dorsolateral prefrontal cortex (DLPFC) and contexts such as emotion and cognitive control, respectively-each consistently converge on similar target regions, thus supporting the reliability of PPI as a tool for studying functional integration. Second, we also found target regions that were specific to the chosen seed region and psychological context, indicating distinct patterns of brain connectivity. For example, the DLPFC seed reliably contributed to a posterior cingulate cortex target during cognitive control but contributed to an amygdala target in other contexts. Our results point to the robustness of PPI while highlighting common and distinct patterns of functional integration, potentially advancing models of brain connectivity. Hum Brain Mapp 37:2904-2917, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- David V Smith
- Department of Psychology, Rutgers University, Newark, New Jersey
| | - Mouad Gseir
- Department of Psychology, Rutgers University, Newark, New Jersey
| | - Megan E Speer
- Department of Psychology, Rutgers University, Newark, New Jersey
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Abstract
For centuries, decision scholars paid little attention to emotions: Decisions were modeled in normative and descriptive frameworks with little regard for affective processes. Recently, however, an “emotions revolution” has taken place, particularly in the neuroscientific study of decision making, putting emotional processes on an equal footing with cognitive ones. Yet disappointingly little theoretical progress has been made. The concepts and processes discussed often remain vague, and conclusions about the implications of emotions for rationality are contradictory and muddled. We discuss three complementary ways to move the neuroscientific study of emotion and decision making from agenda setting to theory building. The first is to use reverse inference as a hypothesis-discovery rather than a hypothesis-testing tool, unless its utility can be systematically quantified (e.g., through meta-analysis). The second is to capitalize on the conceptual inventory advanced by the behavioral science of emotions, testing those concepts and unveiling the underlying processes. The third is to model the interplay between emotions and decisions, harnessing existing cognitive frameworks of decision making and mapping emotions onto the postulated computational processes. To conclude, emotions (like cognitive strategies) are not rational or irrational per se: How (un)reasonable their influence is depends on their fit with the environment.
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Affiliation(s)
- Kirsten G. Volz
- Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Ralph Hertwig
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
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39
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Han H, Chen J, Jeong C, Glover GH. Influence of the cortical midline structures on moral emotion and motivation in moral decision-making. Behav Brain Res 2016; 302:237-51. [PMID: 26772629 DOI: 10.1016/j.bbr.2016.01.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 12/30/2015] [Accepted: 01/03/2016] [Indexed: 11/29/2022]
Abstract
The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions-i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)-and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning.
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Affiliation(s)
- Hyemin Han
- Stanford Graduate School of Education, Stanford University, CA, USA.
| | - Jingyuan Chen
- Department of Electrical Engineering, School of Engineering, Stanford University, CA, USA
| | - Changwoo Jeong
- Department of Ethics Education, College of Education, Seoul National University, Seoul, Republic of Korea
| | - Gary H Glover
- Department of Radiology, School of Medicine, Stanford University, CA, USA
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40
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Zeelenberg R, Pecher D. The Role of Motor Action in Memory for Objects and Words. PSYCHOLOGY OF LEARNING AND MOTIVATION 2016. [DOI: 10.1016/bs.plm.2015.09.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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41
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Wehbe L, Ramdas A, Steorts RC, Shalizi CR. REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING. Ann Appl Stat 2015; 9:1997-2022. [PMID: 34326914 PMCID: PMC8317475 DOI: 10.1214/15-aoas837] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare a suite of approaches which regularize via shrinkage: ridge regression, the elastic net (a generalization of ridge regression and the lasso), and a hierarchical Bayesian model based on small area estimation (SAE). We contrast regularization with spatial smoothing and combinations of smoothing and shrinkage. All methods are tested on functional magnetic resonance imaging (fMRI) data from multiple subjects participating in two different experiments related to reading, for both predicting neural response to stimuli and decoding stimuli from responses. Interestingly, when the regularization parameters are chosen by cross-validation independently for every voxel, low/high regularization is chosen in voxels where the classification accuracy is high/low, indicating that the regularization intensity is a good tool for identification of relevant voxels for the cognitive task. Surprisingly, all the regularization methods work about equally well, suggesting that beating basic smoothing and shrinkage will take not only clever methods, but also careful modeling.
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42
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Abstract
Mental simulation, the process of self-projection into alternate temporal, spatial, social, or hypothetical realities is a distinctively human capacity. Numerous lines of research also suggest that the tendency for mental simulation is associated with enhanced meaning. The present research tests this association specifically examining the relationship between two forms of simulation (temporal and spatial) and meaning in life. Study 1 uses neuroimaging to demonstrate that enhanced connectivity in the medial temporal lobe network, a subnetwork of the brain's default network implicated in prospection and retrospection, correlates with self-reported meaning in life. Study 2 demonstrates that experimentally inducing people to think about the past or future versus the present enhances self-reported meaning in life, through the generation of more meaningful events. Study 3 demonstrates that experimentally inducing people to think specifically versus generally about the past or future enhances self-reported meaning in life. Study 4 turns to spatial simulation to demonstrate that experimentally inducing people to think specifically about an alternate spatial location (from the present location) increases meaning derived from this simulation compared to thinking generally about another location or specifically about one's present location. Study 5 demonstrates that experimentally inducing people to think about an alternate spatial location versus one's present location enhances meaning in life, through meaning derived from this simulation. Study 6 demonstrates that simply asking people to imagine completing a measure of meaning in life in an alternate location compared with asking them to do so in their present location enhances reports of meaning. This research sheds light on an important determinant of meaning in life and suggests that undirected mental simulation benefits psychological well-being.
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Affiliation(s)
- Adam Waytz
- Kellogg School of Management, Northwestern University
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43
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Abstract
When measured with functional magnetic resonance imaging (fMRI) in the resting state (R-fMRI), spontaneous activity is correlated between brain regions that are anatomically and functionally related. Learning and/or task performance can induce modulation of the resting synchronization between brain regions. Moreover, at the neuronal level spontaneous brain activity can replay patterns evoked by a previously presented stimulus. Here we test whether visual learning/task performance can induce a change in the patterns of coded information in R-fMRI signals consistent with a role of spontaneous activity in representing task-relevant information. Human subjects underwent R-fMRI before and after perceptual learning on a novel visual shape orientation discrimination task. Task-evoked fMRI patterns to trained versus novel stimuli were recorded after learning was completed, and before the second R-fMRI session. Using multivariate pattern analysis on task-evoked signals, we found patterns in several cortical regions, as follows: visual cortex, V3/V3A/V7; within the default mode network, precuneus, and inferior parietal lobule; and, within the dorsal attention network, intraparietal sulcus, which discriminated between trained and novel visual stimuli. The accuracy of classification was strongly correlated with behavioral performance. Next, we measured multivariate patterns in R-fMRI signals before and after learning. The frequency and similarity of resting states representing the task/visual stimuli states increased post-learning in the same cortical regions recruited by the task. These findings support a representational role of spontaneous brain activity.
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44
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Increasing the role of belief information in moral judgments by stimulating the right temporoparietal junction. Neuropsychologia 2015; 77:400-8. [PMID: 26375450 DOI: 10.1016/j.neuropsychologia.2015.09.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 08/28/2015] [Accepted: 09/10/2015] [Indexed: 11/22/2022]
Abstract
Morality plays a vital role in our social life. A vast body of research has suggested that moral judgments rely on cognitive processes mediated by the right temporoparietal junction (rTPJ), an area thought to be involved in belief attribution. Here we assessed the role of the rTPJ in moral judgments directly by means of transcranial direct current stimulation (tDCS)--a non-invasive brain stimulation technique that, by applying a weak current to the scalp, allows modulating cortical excitability of the area being stimulated. Participants were randomly and equally assigned to receive anodal stimulation (to increase cortical excitability), cathodal stimulation (to decrease cortical excitability), or sham (placebo) stimulation over the rTPJ before completing a moral judgment task. Participants read stories in which protagonists produced either a negative or a neutral outcome based on either a negative or a neutral belief that they were causing harm or no harm, respectively. Results revealed a selective group difference when judging the moral permissibility of accidental harms (belief neutral, outcome negative), but not intentional harms (belief negative, outcome negative), attempted harms (belief negative, outcome neutral), or neutral acts (belief neutral, outcome neutral). Specifically, participants who received anodal stimulation assigned less blame to accidental harms compared to participants who received cathodal or sham stimulation. These results are consistent with previous findings showing that the degree of rTPJ activation reflects reliance on the agent's innocent intention. Crucially, our findings provide direct evidence supporting the critical role of the rTPJ in mediating belief attribution for moral judgment.
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45
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Gonsalves BD, Cohen NJ. Brain Imaging, Cognitive Processes, and Brain Networks. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2015; 5:744-52. [PMID: 26161888 DOI: 10.1177/1745691610388776] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The recent, rapid expansion of the application of neuroimaging techniques to a broad variety of questions about the structure and function of mind and brain has led to much necessary and often critical introspection about what these techniques can actually tell us about cognitive processes. In this article, we attempt to place neuroimaging within the broader context of the cognitive neuroscience approach, which emphasizes the benefits of converging methodologies for understanding cognition and how it is supported by the functioning of the brain. Our arguments for what neuroimaging has to offer are supported by two specific examples from research on memory that, we believe, show how neuroimaging data have provided unique insights not only into brain organization, but also into the organization of the mind.
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Affiliation(s)
- Brian D Gonsalves
- Department of Psychology and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Neal J Cohen
- Department of Psychology and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
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46
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Abstract
AbstractPessoa (2013) makes a compelling case for conceiving of emotion and cognition as deeply integrated processes in the brain. We will begin our commentary by asking what implications this view of the brain has for an ontology of cognition – a theory of what cognition is and what cognitive processes exist. We will suggest that Pessoa's book, The Cognitive-Emotional Brain, provides strong support for an embodied theory of cognition. We end our commentary by offering some speculation about how Pessoa's arguments naturally extend to social cognition.
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47
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Abstract
Grounded-cognition theories suggest that memory shares processing resources with perception and action. The motor system could be used to help memorize visual objects. In two experiments, we tested the hypothesis that people use motor affordances to maintain object representations in working memory. Participants performed a working memory task on photographs of manipulable and nonmanipulable objects. The manipulable objects were objects that required either a precision grip (i.e., small items) or a power grip (i.e., large items) to use. A concurrent motor task that could be congruent or incongruent with the manipulable objects caused no difference in working memory performance relative to nonmanipulable objects. Moreover, the precision- or power-grip motor task did not affect memory performance on small and large items differently. These findings suggest that the motor system plays no part in visual working memory.
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48
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Diekmann J, König CJ, Alles J. The Role of Neuroscience Information in Choosing a Personality Test: Not as seductive as expected. INTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT 2015. [DOI: 10.1111/ijsa.12099] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Janina Diekmann
- Department of Psychology; Saarland University; Campus A1 3 66123 Saarbrücken Germany
| | - Cornelius J. König
- Department of Psychology; Saarland University; Campus A1 3 66123 Saarbrücken Germany
| | - Julia Alles
- Department of Psychology; Saarland University; Campus A1 3 66123 Saarbrücken Germany
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49
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Goksan S, Hartley C, Emery F, Cockrill N, Poorun R, Moultrie F, Rogers R, Campbell J, Sanders M, Adams E, Clare S, Jenkinson M, Tracey I, Slater R. fMRI reveals neural activity overlap between adult and infant pain. eLife 2015; 4. [PMID: 25895592 PMCID: PMC4402596 DOI: 10.7554/elife.06356] [Citation(s) in RCA: 135] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 03/11/2015] [Indexed: 12/15/2022] Open
Abstract
Limited understanding of infant pain has led to its lack of recognition in clinical practice. While the network of brain regions that encode the affective and sensory aspects of adult pain are well described, the brain structures involved in infant nociceptive processing are less well known, meaning little can be inferred about the nature of the infant pain experience. Using fMRI we identified the network of brain regions that are active following acute noxious stimulation in newborn infants, and compared the activity to that observed in adults. Significant infant brain activity was observed in 18 of the 20 active adult brain regions but not in the infant amygdala or orbitofrontal cortex. Brain regions that encode sensory and affective components of pain are active in infants, suggesting that the infant pain experience closely resembles that seen in adults. This highlights the importance of developing effective pain management strategies in this vulnerable population. DOI:http://dx.doi.org/10.7554/eLife.06356.001 Doctors long believed that infants do not feel pain the way that older children and adults do. Instead, they believed that the infants' responses to discomfort were reflexes. Based on these beliefs, it was a routine practice to perform surgery on infants without suitable pain relief up until the late 1980s. Even now, infants may receive less than ideal pain relief. For example, a review found that although newborns in intensive care units undergo 11 painful procedures per day on average, more than half of the babies received no pain medications. Some guidelines continue to emphasize that for infants cuddling and feeding are more important sources of comfort than pain-relieving drugs. There is growing support for better pain control for infants. Doctors and nurses now routinely observe behaviour and physiological responses—such as heart rate—to assess whether infants are experiencing pain. When an infant shows signs of pain, medical staff may give the infant sugar water or other interventions aimed at reducing their distress. However, recordings of brain activity suggest that infants may experience pain without exhibiting physical signs and that sugar water may reduce the behaviours associated with pain but not the pain itself. More objective measurements of infant pain would be useful, but to create such measurements scientists must first understand how infants experience pain. So Goksan et al. used a technique called functional magnetic resonance imaging (fMRI) to compare the brain responses of adults and newborns to the same stimulus—a sharp poke of the foot. The adults were also asked about the pain they experienced, and whether the infants pulled their foot away when poked was documented. The fMRI results revealed that pain increased activity in 20 regions in the adults' brains, and 18 of the same regions in the infants' brains. The brain regions activated in the infants' brains in response to a poke on the foot are involved in processing sensations and emotions. The two regions that did not activate in the infant brains—the amygdala and the orbitofrontal cortex—help individuals interpret the stimuli. Goksan et al. therefore conclude that infants experience pain in similar ways to adults, though they may not experience all the emotions that adults have when they are in pain. It is, therefore, important to give infants suitable pain relief during potentially painful procedures. DOI:http://dx.doi.org/10.7554/eLife.06356.002
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Affiliation(s)
- Sezgi Goksan
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Caroline Hartley
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Faith Emery
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Naomi Cockrill
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Ravi Poorun
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fiona Moultrie
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Richard Rogers
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jon Campbell
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Michael Sanders
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Eleri Adams
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Stuart Clare
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Irene Tracey
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Rebeccah Slater
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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50
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Breiter HC, Block M, Blood AJ, Calder B, Chamberlain L, Lee N, Livengood S, Mulhern FJ, Raman K, Schultz D, Stern DB, Viswanathan V, Zhang FZ. Redefining neuromarketing as an integrated science of influence. Front Hum Neurosci 2015; 8:1073. [PMID: 25709573 PMCID: PMC4325919 DOI: 10.3389/fnhum.2014.01073] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 12/29/2014] [Indexed: 11/13/2022] Open
Abstract
Multiple transformative forces target marketing, many of which derive from new technologies that allow us to sample thinking in real time (i.e., brain imaging), or to look at large aggregations of decisions (i.e., big data). There has been an inclination to refer to the intersection of these technologies with the general topic of marketing as "neuromarketing". There has not been a serious effort to frame neuromarketing, which is the goal of this paper. Neuromarketing can be compared to neuroeconomics, wherein neuroeconomics is generally focused on how individuals make "choices", and represent distributions of choices. Neuromarketing, in contrast, focuses on how a distribution of choices can be shifted or "influenced", which can occur at multiple "scales" of behavior (e.g., individual, group, or market/society). Given influence can affect choice through many cognitive modalities, and not just that of valuation of choice options, a science of influence also implies a need to develop a model of cognitive function integrating attention, memory, and reward/aversion function. The paper concludes with a brief description of three domains of neuromarketing application for studying influence, and their caveats.
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Affiliation(s)
- Hans C Breiter
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine Chicago, IL, USA ; Mood and Motor Control Laboratory or Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital Boston, MA, USA ; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA
| | - Martin Block
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Medill Integrated Marketing Communications, Northwestern University Evanston, IL, USA
| | - Anne J Blood
- Mood and Motor Control Laboratory or Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital Boston, MA, USA ; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA
| | - Bobby Calder
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Department of Marketing, Kellogg School of Management, Northwestern University Evanston, IL, USA
| | - Laura Chamberlain
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Aston Business School Birmingham, UK
| | - Nick Lee
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; School of Business and Economics, Loughborough University Leicestershire, UK
| | - Sherri Livengood
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine Chicago, IL, USA ; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA
| | - Frank J Mulhern
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Medill Integrated Marketing Communications, Northwestern University Evanston, IL, USA
| | - Kalyan Raman
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine Chicago, IL, USA ; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Medill Integrated Marketing Communications, Northwestern University Evanston, IL, USA
| | - Don Schultz
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Medill Integrated Marketing Communications, Northwestern University Evanston, IL, USA
| | - Daniel B Stern
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine Chicago, IL, USA ; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA
| | - Vijay Viswanathan
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Medill Integrated Marketing Communications, Northwestern University Evanston, IL, USA
| | - Fengqing Zoe Zhang
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University Evanston, IL, USA ; Department of Statistics, Northwestern University Evanston, IL, USA ; Department of Psychology, Drexel University Philadelphia, PA, USA
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